The Internet of Things (IoT) and Machine Learning are two of the hottest technologies of our time. They have made possible things that were totally inconceivable a few years ago, and promise to continue transforming our lives in ways that are unimaginable at present. Smart appliances, cars, homes, cities and smart-about-everything are making our lives faster and more efficient. But are we considering the tradeoffs? Are we ready to embrace the dramatic changes that will overcome our lives in a few years? More precisely, are we ready for machines starting to decide for us and us becoming dumb people who rely on smart things to carry on with their lives?

Startup Blue River Technology is building agricultural robots that allow farmers to assess their crops plant by plant, aided by the deep learning technology from Nervana Systems. Nervana, in effect, is an AI-as-a-service company aimed at expanding deep learning beyond a few big companies and research institutions with the deep pockets and expertise to use it. Nervana recently launched a cloud service to offer developers and data scientists the tools and infrastructure to make machine intelligence dramatically faster, easier and more scalable. Blue River's robots are used in the field for tasks such as thinning lettuce. It had developed a 3-D system for phenotyping corn plants that became flummoxed by things like weeds.

Seattle is poised to be an epicenter for machine learning and artificial intelligence. That's one takeaway from the inaugural Machine Learning / Artificial Intelligence Summit hosted by Madrona Venture Group on Wednesday in downtown Seattle. Thanks to Amazon Web Services, Microsoft Azure and a wide range of startups, the Emerald City is already known as a hub for cloud computing technology development, with Madrona Managing Director Matt McIllwain calling Seattle the "cloud capital of the world" more than two years ago. There is also a long list of Silicon Valley tech companies who have established engineering outposts in the Seattle area, including Google, Facebook, Oracle, Apple, HP, Uber, Lyft, Twitter, Splunk, and many others. But looking beyond the infrastructure and services side of cloud computing, there is also momentum building from both tech giants and small startups in Seattle that are developing technologies related to machine learning, artificial intelligence, natural language processing, algorithms, data analytics, and more.

About Author The second phase of Delhi's odd-even rule ended Saturday, but restrictions on "surge pricing" used by cab aggregators Ola and Uber to meet demand and supply is not expected to end till the state government issues sector-specific guidelines. Maitreya One, a black futurist and hip-hop artist living in Harlem, steps off the Greyhound bus on a warm morning in Montgomery, Alabama. I walk up to him and give him a hug. Nightmare scenarios involving Artificial Intelligence typically involve computers that become too smart for their own good and turn against their creators. In 2001: A Space Odyssey, HAL 9000 famously refused to open the pod bay doors for Dave: Well, now we have an entirely different cause to be wary of AI, and the culprit is human rather than machine.

Computer scientists are not often invited to present their research at The Science of Consciousness annual conference, but University of California San Diego development engineer Stephen Deiss did just that. He spoke to the meeting in Tucson, AZ, in late April on the subject of "Romancing the Oxymoron: The'Hardware Problem' of Machine Consciousness." "I presented my view that our preconceptions – about causality and mechanisms – bias us against accepting the possibility that machines can be conscious," said Deiss (at left), who earned his M.S. in computer science at Purdue University. "It leaves us believing that there is something spooky and unnatural about our own awareness, but I argue that consciousness is fundamental and scale-free in nature." Supported by CSE since 2013, Deiss first worked with the Non-Volatile Systems Laboratory of CSE Prof. Steven Swanson, but now splits his time between the Integrated Systems Neuroengineering Lab of Bioengineering professor Gert Cauwenberghs, and the new Pattern Recognition Laboratory of the Qualcomm Institute (both with CSE support). "We are focused on neurally-inspired or otherwise non-von Neumann computing paradigms," said Deiss, referring to new architectures that, unlike most of today's computers, are not based on executing instructions sequentially.

An Arlington, Va.-based company called Deep Learning Analytics has developed a deep machine learning automatic target recognition (ATR) program prototype for DARPA that will help assist aircraft pilots in finding targets.Deep Learning Analytics is nine months into… You must be logged in as a subscriber to view this page. Please log in below to access the content. If you are already a Defense Daily subscriber or registered user, login here.

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree.

The creators of Siri have announced a brand new creation from their brilliant minds, the artificial intelligence assistant named Viv. Apple based Siri has become both a technological powerhouse, and a cultural touchstone. Now the creators behind it have moved on to the next stage. Viv is a more advanced AI that works by connecting to multiple sources of information and drawing responses right from the pools of data themselves. No more regurgitating web search results; this assistant is smarter, sleeker, and more accurate.

A year ago the Raptors were victims of an unexpected 4-0 sweep in the first round of the NBA playoffs at the hands of the lower-ranked Washington Wizards. They're now tied with the Miami Heat 2-2 in a grueling best-of-seven series, which will see the eventual winners play LeBron James and the Cleveland Cavaliers in the Eastern Conference finals. Maple Leaf Sports & Entertainment, which owns the Raptors, announced in February that it would use cognitive analysis provided by IBM's Watson technology platform, noting that Watson would be used mostly for talent acquisition. Is IBM Watson one of the factors behind the improved performance? It's difficult to answer that question because the Raptors consider the IBM agreement to be a competitive advantage, and are therefore mum on this subject.

Thank you to Rahul Iyer from Pivotal for contributing to the software and to this article. Path functions are a powerful capability in the data science toolkit, and they are now available in the newest release of the open source Apache MADlib (incubating) library. For example, path functions can be used to reason over website, shopping cart, and customer support clickstreams to identify the golden paths to purchase, multi-channel promotion effectiveness, or customer churn. In addition, they can be used in predictive analytics use cases, like analyzing millions of sensor logs from cars or other machines to identify common patterns in part failure. These scenarios can also improve safety and substantially lower operating costs.